Human thinking is mostly language-independent. However, human information exchange is massively language dependent, therefore the process of information retrieval is obviously language dependent too. Natural languages are still not commonly used as information retrieval languages, as despite that the idea itself seems to be promising, it poses several serious problems as well. Nevertheless, there have been scientific researches and achievements targeting this domain since the early 90's. Traditional information retrieval languages have rather poor syntactic structure compared to natural languages. Many times, vaguely represented queries can be replied only by 'noisy' irrelevant answers filled with imperfect information. In this case, progress can only be expected from information retrieval languages with more versatile syntax, which can model not only discrete concepts (or their conjuncts), but their natural language relations as well. The paper is going to outline such a new type of information retrieval language with a better expressive power. The paper shows that there are knowledge representation languages which can faithfully represent the logical fine structure of natural languages, and to which both natural language texts and background knowledge (ontologies, thesauri) can easily (almost automatically) be translated, while the query itself can also be represented by the same language. A full explanation can be given by a step-by-step tracing of the generated answer.
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.